__The decoder is a mirror image of the encoder, except instead of convolutional layers, we use convolutional transpose layers, as shown in the code. __

(Actually we don't have to write decoder as a mirror image of the encoder. But it is inevitable for the decoder to have exactly same size for the output image as we the input size of the encoder)

     #<1> define the input to the decoder (the point in the latent space).
     decoder_input = Input(shape=(self.z_dim,), name='decoder_input')

        x = Dense(np.prod(shape_before_flattening))(decoder_input)
        x = Reshape(shape_before_flattening)(x)

        for i in range(self.n_layers_decoder):
            conv_t_layer = Conv2DTranspose(
                filters = self.decoder_conv_t_filters[i]
                , kernel_size = self.decoder_conv_t_kernel_size[i]
                , strides = self.decoder_conv_t_strides[i]
                , padding = 'same'
                , name = 'decoder_conv_t_' + str(i)
                )

            #<2>Stack convolutional transpose layers on top of each other.
            x = conv_t_layer(x)

            if i < self.n_layers_decoder - 1:
                x = LeakyReLU()(x)
                
                if self.use_batch_norm:
                    x = BatchNormalization()(x)
                
                if self.use_dropout:
                    x = Dropout(rate = 0.25)(x)
            else:
                x = Activation('sigmoid')(x)

        decoder_output = x
         
        #<3>The Keras model that defines the decoder—a model that takes a point in t 
        #the latent space and decodes it into the original image domain.
        self.decoder = Model(decoder_input, decoder_output)```